Tracking error prediction informed motion control of a parallel machine tool for high-performance machining

Parallel machine tools (PMTs) are suitable for machining parts with complex surface features, owing to their advantages in providing flexible attitude adjustments and quick dynamic responses. However, due to the characteristics of the multiple-axis coupling motions and nonlinear dynamics, how to achieve high-efficiency and high-quality machining using PMTs remains a challenging issue encountered in the field. To solve this problem, in this study, the tracking error generation mechanism for PMTs is originally disclosed, which indicates that the driving limb's tracking error includes the error caused by the time-varying load, and that caused by the input signal. Accordingly, an integrated control method combining real-time dynamic feedforward and feedrate scheduling is proposed for reducing these two parts of the tracking errors. A stepwise identification method that can decouple the friction parameters and inertial parameters is designed for real-time dynamic feedforward control. In addition, by establishing systematic machining quality constraints related to the PMT's dynamic properties, a feedrate scheduling method for complex spline toolpaths is put forward. Finally, toolpath tracking and machining comparison experiments are conducted with a commercial ISG computer numerical control (CNC) kernel to validate the advantages of the proposed control method. The experimental results demonstrate that the proposed integrated control method can effectively improve the machining efficiency and machining quality. In summary, this study analytically formulates a tracking error prediction model for PMTs, and proposes an integrated control method for high-performance machining. The findings of this study provide a feasible way to improve the performance of PMTs, and to further promote their popularization and application in industry.